Cryptolepis sanguinolenta have been investigated for their ability to inhibit two of the main proteins in SARS-CoV-2, the main protease and the RNA-dependent RNA polymerase, using in silico methods. Molecular docking was used to assess binding potential of the alkaloids to the viral proteins whereas molecular dynamics was used to evaluate stability of the binding event. The results of the study indicate that all 13 alkaloids bind strongly to the main protease and RNA-dependent RNA polymerase with binding energies ranging from -6.7 to -10.6 kcal/mol. In particular, cryptomisrine, cryptospirolepine, cryptoquindoline, and biscryptolepine exhibited very strong inhibitory potential towards both proteins. Results from the molecular dynamics study revealed that a stable protein-ligand complex is formed upon binding. Alkaloids from Cryptolepis sanguinolenta therefore represent a promising class of compounds that could serve as lead compounds in the search for a cure for the corona virus disease.The ongoing global pandemic caused by the human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected millions of people and claimed hundreds of thousands of lives. The absence of approved therapeutics to combat this disease threatens the health of all persons on earth and could cause catastrophic damage to society. New drugs are therefore urgently required to bring relief to people everywhere. In addition to repurposing existing drugs, natural products provide an interesting alternative due to their widespread use in all cultures of the world. In this study, alkaloids from  The novel human coronavirus was reported in China in late 2019. Ever since, the virus has spread throughout the world and has been designated a pandemic . As at Mbeta-coronavirus. These coronaviruses possess single strands of RNA and are enveloped in a protein with spike-like structures that project out of the surface of the envelope. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) as a receptor during infection alanyl-l-valyl-N~1~-4-(benzyloxy)-4-oxo-1-{[(3R)-2-oxopyrrolidin-3-yl]methyl}but-2-enyl)-L-leucinamide)[pro were labelled with their original subdomain classification S1 , S1′ , S2 , and S4  for simplicity.The X-ray crystal structure of SARS-CoV-2 main protease (Mcinamide). N3 was cinamide) entry foHomology model of the RNA-dependent RNA polymerase of SARS-CoV-2 was accessed from the SWISS-MODEL web server with RefSeq ID YP_009725307.1 , 25. A sThe proteins were prepared by removing all complexed ligands and maintaining water molecules, especially those in the active sites where applicable. Replacement of incomplete side chains was done using the default Dunbrack rotamer library. Protonation states were assigned for histidine while addition of polar hydrogen atoms to correct the calculation of partial charges for standard residues was also done. Rotamer and backbone secondary structure preferences were improved with AMBER ff14SB, and then Gasteiger charges were computed for each atom using Antechamber implemented in Chimera , 29.Cryptolepis sanguinolenta  alkaloids isolated from inolenta  were usein silico docking represent correctly bound conformations, it was visually inspected and compared to the experimentally determined binding modes and conformations of N3 in 6LU7. The DINC 2.0 web server was used to dock N3 with Mpro using estimated grid dimensions provided by the software since N3 is peptidic in nature [pro validated the docking protocol used in the study. For docking involving non-peptide ligands, AutoDock Vina was used. The molecular docking studies were performed on all 13 ligands against Mpro using the following grid dimensions: center (Å): X: -10.7647, Y: 12.5092, and Z: 68.969 and size (Å): X: 28, Y: 28, and Z: 28. For RdRp and RdRpol, docking of ligands with the proteins was performed using the following grid dimensions: center (Å): X: 132.394, Y: 137.038, and Z: 165.346 and size (Å): X: 41, Y: 41, and Z: 41 for RdRp and center (Å): X: 123.115, Y: 115.699, and Z: 132.978 and size (Å): X: 41, Y: 41, and Z: 41 for RdRpol.Precision docking protocol was developed by docking the bound ligand into the prepared active sites. In order to verify that the pose resulting from n nature . The preKd) and ligand efficiency (Δg) [In all cases, docking was performed in three technical runs. Free energies were obtained from runs with the most consistent poses. These free energies were used for the estimation of the ligand binding constant (ncy (Δg) . Proteinpro whereas cryptomisrine, cryptospirolepine, and cryptoquindoline were used in MD simulation studies of RdRp and RdRpol. The Ligand and Receptor Molecular Dynamics (LARMD) web server was utilized for all MD simulations. The default MD simulation method on the LARMD web server was modified to suit the purpose of this study. The explicit water model was used in the simulation. Where necessary, Na+ or Cl− was added to obtain a neutral system. Trajectory analysis and free energy calculation protocols were used in the default mode as found on the LARMD web server [Ligands with binding affinities between -8.5 and -11.0 kcal/mol were considered for molecular dynamics (MD) simulations. In this regard, cryptomisrine, cryptospirolepine, cryptoquindoline, and biscryptolepine were used for MD simulation studies of Mb server .Protein-ligand complexes were written from the best pose obtained from Vina. Ligand atoms were assigned AM1-BCC charges using the Antechamber module whereas the coordinate and topology files of the complex were constructed with the teLeap module, all in the AMBER16 package , 34. TheQuantitative structure-to-activity relationship (QSAR) properties were estimated using Molsoft and the SwissADME web servers . ToxicitCryptolepis sanguinolenta is one such plant with antiviral, antiplasmodial, anti-inflammatory, and hypotensive capabilities [in silico studies.The search for therapeutic agents to combat the current COVID-19 pandemic is of utmost importance to all nations of the world. The absence of vaccines and potent antiviral agents against SARS-CoV-2 and effective treatment regimens amongst other factors has led to a significant increase in global mortality due to COVID-19. To date, the major strategy for identifying new COVID-19 drug candidates has been via drug repurposing, aided by high throughput screening of existing antiviral agents. Reliance on plant-derived natural products and formulations for the treatment of various diseases has proven beneficial globally. Extracts and compounds isolated from many of these plants have been shown to possess various pharmacological activities. bilities . Due to bilities , and thein vitro inhibitor in the crystal structure. The presence of the ligand provides a simple route for the identification of binding pockets, selection of grid coordinates, and validation of docking protocols. The active site residues of this protease with a Cys-His catalytic dyad were identified. Two different structures were used for the RNA-dependent RNA polymerase. Initially, an experimental 3D structure of this protein was unavailable and was only obtained via homology modeling. A valid, high quality homology model of the RNA-dependent RNA polymerase was constructed by the SWISS-MODEL web server. The sequence similarity between RNA-dependent RNA polymerase and the template, 6NUR, on which the homology model was based was 96.35% [The protein targets selected were the main protease and the RNA-dependent RNA polymerase primarily due to the key roles these proteins play in viral replication and sustainability as well as the availability of experimental structures in the protein database. Two different groups have recently solved the structures of the main protease of SARS-CoV-2 , 8. Thess 96.35% , 25. Thes 96.35% , 41. Recs 96.35% . Comparipro, was docked against N3. N3 is the ligand co-crystallized with Mpro in the crystal structure. Comparison of its docked output with the native revealed comparable pocket interactions. Interactions of N3 with Met49, Thr190, Gln189, Glu166, Ala191, Leu141, His41, Met165, His41, His163, and Gly143 were identified in both conformations  and ATP  had similar binding affinities towards RdRpol. Interactions observed in the docking of the two ligands with RdRp and RdRpol were also similar Fig. .pro and RdRp and those reported were similar. Slight differences in results may be attributed to variation in software and computing power [pro, -5.4 kcal/mol for RdRp, and -5.9 kcal/mol for RdRpol), as shown in Finally, docking of some ligands reported in the literature against the main protease or the RNA-dependent RNA polymerase was also carried out. The binding affinities obtained from the docking of these ligands with Mng power . Ligand Cryptolepis sanguinolenta were docked against Mpro, RdRp, and RdRpol to identify possible binding interactions between proteins and the alkaloids. Against Mpro, the binding energies observed for all 13 alkaloids were between -6.9 and -10.6 kcal/mol  leading to a resultant positively charged nitrogen  for cryptomisrine, cryptospirolepine, cryptoquindoline, and biscryptolepine were well below 1 μM for both RdRp and RdRpol, indicating strong association between ligands and protein targets. Cryptomisrine and cryptospirolepine exhibited much stronger binding to Mpro in contrast to both RdRp and RdRpol. Ligand efficiencies for all 13 alkaloids were very similar for Mpro, RdRp, and RdRpol  of ligands in the Mpro-ligand complexes was between 0.39 and 0.62 Å and that of the protein (MPro) was in the range of 1.43–1.95 Å. The radius of gyration for all Mpro-ligand complexes was about 22 Å. For both RdRp and RdRpol, the ligands involved in the protein-ligand complexes investigated were cryptomisrine, cryptospirolepine, and cryptoquindoline. The ligand and protein RMSD for the RdRp complexes ranged between 0.30 and 0.69 Å and 1.80 and 1.87 Å, respectively. For the RdRpol complexes, the ligand and protein RMSD ranged between 0.42-0.51 Å and 2.94-3.96 Å, respectively. It is interesting to note that protein RMSD was much greater in RdRpol complexes (>2 Å) than in both RdRp and Mpro complexes  may also have contributed to its conformational stability shown in minimal overall configurational entropy. The radius of gyration for RdRp and RdRpol complexes was about 29 Å and 32 Å, respectively. However, the radius of gyration for the cryptospirolepine-RdRpol complex was much higher, at 38.65 Å. Entropic estimation for experimental structures were positive in Mpro  and RdRpol , except for those of RdRp (Table G/PBSA and GBSA). Free energy contribution arising from electrostatic interactions with pocket residues was less in contrast to that of remdesivir triphosphate (RemTP) affecting the end point free energies  were obtained for the best four tight binding alkaloids assessed. These three alkaloids were less toxic than lopinavir . Overall, these predictions indicate that these molecules taken together would be safe. Cryptoquindoline was predicted to be strongly mutagenic (probability 0.8), cryptomisrine was moderate  while in contrast, cryptospirolepine and biscryptolepine were not potential mutagens or carcinogens (Table Cryptolepis sanguinolenta are well documented in the literature [The QSAR and ADMET property and probability predictions were made to assess the key drug-like qualities of some of the alkaloids used. Cryptomisrine had the most hydrogen bond donors followed by biscryptolepine and hydroxycryptolepine. Cryptoheptine had the ability to accept the most hydrogen bonds followed by cryptoquindoline and quindoline. With a similar molecular landscape, all 13 alkaloids were largely hydrophobic, with most being moderately soluble to poorly soluble. Only hyroxycryptolepine was predicted to be completely soluble. All ligands have the ability to cross the blood-brain barrier with most having high gastrointestinal absorption indices . In the ns Table . Toxicitterature –52, and terature . It has terature , 51. InvCryptolepis sanguinolenta have exhibited high binding affinity and hence potential inhibitory activity towards two of the major prions of SARS-CoV-2, the main protease and the RNA-dependent RNA polymerase. The high binding affinity of cryptomisrine, cryptospirolepine, cryptoquindoline, and biscryptolepine for both protein targets, drug-like characteristics, tight binding, and residence times within protein binding pockets makes them ideal candidates for further in vitro and in vivo validation.The use of natural plant-based extracts as antiviral agents is well established in the literature. One advantage of using plant extracts for therapeutic purposes is the potential for synergism by the compounds of that extract. As shown in this molecular docking study, the different alkaloids of


Long noncoding RNAs (lncRNAs) played a crucial role in a number of biological processes. lncRNA HAGLROS was demonstrated to facilitate cell proliferation and migration in various cancers. However, the functions and molecular mechanisms of HAGLROS in osteosarcoma remained to be elucidated.  qRT-PCR assay was used to detect the relative expression of HAGLROS in osteosarcoma tissue samples and cells. CCK-8 and Transwell assays were performed to assess the effects of HAGLROS on OS cells proliferation and invasion. Luciferase reporter assay verified the interaction between ROCK1 and miR-152.  In our study, we found that the expression of HAGLROS increased osteosarcoma samples and cell lines compared with normal tissues and cells. HAGLROS knockdown inhibited certain functions of U2OS and SW1353 cells in vitro. Moreover, HAGLROS depletion inhibited tumor growth and metastasis in vivo. Mechanically, we found that HAGLROS sponged miR-152 to promote ROCK1 expression in U2OS and SW1353 cells.  In summary, our study indicated that HAGLROS could promote osteosarcoma progression by sponging miR-152 to promote ROCK1 expression. The results showed HAGLROS/miR-152/ROCK1 axis might act as a novel therapeutic strategy for osteosarcoma. However, there is still lacking the understanding of the pathogenic mechanisms related to osteosarcoma.Osteosarcoma (OS) is a rare malignant tumor with low incidence, frequent onset in adolescents and the elderly, high metastasis, and poor prognosis . In addiThe genetic information in DNA is transcribed, translated, and modified into proteins, which participates in regulating a series of physiological metabolic activities of cells. Interestingly, more than 95% of the human genomes could not be translated into proteins. Recent studies revealed that these long noncoding RNAs had an important role in the regulation of physiological activities in human cancers. Long noncoding RNA (lncRNA) is composed of more than 200 nucleotides . lncRNAsIn this study, we found that the proliferation of OS cells was inhibited after knockdown of HAGLROS. The metastasis of OS cells was also reduced after knockdown of HAGLROS. Furthermore, it was found that lncRNA HAGLROS could interact with miR-152 to affect the expression of downstream gene ROCK1.10 paired OS tissues and match normal bone tissues were collected from the OS patients with surgical treatment from Minhang Hospital, Fudan University, from January 2014 to May 2017. All tissues were stored in liquid nitrogen and used for extraction of RNA. Informed consent was obtained from all patients. This project was approved by the Institute Research Ethics Committee at Minhang Hospital, Fudan University .2 at 37°C.MG-63, hFOB 1.19, SW1353, and U2OS were obtained from ATCC and cultured in RPMI-1640  containing 10% FBS  with 5% COSmall interfering RNAs (siRNAs) specifically targeting HAGLROS were purchased from the GenePharma Company . The siRNA sequences were 5′-CCUAUUUACUGGCAGGAGUTT-3′ for HAGLROS; 5′-UUCUCCGAACGUGUCACGUTT-3′ for NC. Transfections were performed using Lipofectamine 2000 (Life Technologies) according to the manufacturer's instructions.μL CCK-8  was added to each well and then incubated at 37°C for 2 hours, and the optical density (OD) of each well at 450 nm was detected by using a MB-580 machine  [In order to detect the cell proliferation capacity, about 2000 SW1353 or U2OS cells with different treatments were seeded into a 96-well plate. After a certain time, 10 6 cells/well were added into the upper chamber of Transwell with (for invasion assay) or without (for migration assay) diluted Matrigel treatment  and then in the bottom chamber were added complete medium. After incubation for 2 days, the migrating cells were treated with methanol and stained with 10 μg/mL DAPI . The migration or invasion cells were counted using an inverted fluorescence microscope.Briefly, the SW1353 and U2OS cells were starved for 12 hours in RPMI-1640 medium without FBS. Cells at 1 × 10The pMIR-REPORT-HAGLROS-WT/MUT and pMIR-REPORT-ROCK1-3′UTR-WT/MUT  were constructed. Furthermore, the Dual-Luciferase Reporter Assay System  was used to detect the luciferase activities of these reporters according to the manufacturer's protocol [ΔΔCt- method with GAPDH as an internal reference.We extracted RNA by using TRIzol reagent, as described by the manufacturer's protocol . cDNAs were synthesized with the Primer-Script™ one-step RT-PCR kit . The PCR amplification was performed on an Applied Biosystems 7900HT . The relative quantification values of lncRNA were determined by the 2t-test, Fisher's exact test, or Mann–Whitney's U-test were used as appropriate. p < 0.05 was regarded as statistically significant.Statistical analysis was conducted with the GraphPad Prism 6.0 software . To analyze the relationship between gene expression and the clinical characteristics of the tumors, the chi-squared test, p < 0.05). Furthermore, by using qRT-PCR assay, we revealed that HAGLROS was upregulated in MG-63, SW1353, and U2OS cells compared to hFOB 1.19 cells (p < 0.05). In order to further confirm these findings, we analyzed HAGLROS expression in TCGA and found that HAGLROS was upregulated in OS samples compared to normal tissues . CCK-8 assay showed that the proliferation rate of HAGLROS knockdown group was significantly decreased compared with the negative control group in both SW1353 and U2OS cells .In order to further explore the molecular roles of HAGLROS in OS, we conducted functional validation in OS. We observed the HAGLROS expression was reduced in SW1353 and U2OS cells treated with si-HAGLROS which was designed to knock down HAGLROS Figures  .In order to further investigate the effects of HAGLROS on the metastasis capacity of SW1353 and U2OS cells, the Transwell assay showed both the migration and invasion abilities were remarkably suppressed after knockdown HAGLROS in SW1353 and U2OS cells compared with negative controls Figures  (p < 0.0p < 0.05). siRNA-mediated HAGLROS knockdown contributed to miR-152 induction in SW1353 and U2OS cells (p < 0.001). The transfection efficiency after overexpression or knockdown of miR-152 was shown in Figures p < 0.05).Next, we explored the location of this lncRNA in OS to understand its molecular mechanisms. As presented in OS cells  (p < 0.0p < 0.05). Dual-luciferase reporter assay showed HEK 293T cells transfected with miR-152 and wild-type ROCK1 had significantly reduced luciferase activity, but not the mutant ROCK1 (p < 0.05). From the rescue assay, miR-152 inhibitor countervailed HAGLROS knockdown-mediated downregulation on ROCK1 expression in both SW1353 and U2OS cells . The above experimental results could confirm that HAGLROS served as a sponge of miR-152 to promote ROCK1.Furthermore, we aimed to validate the effects of HAGLROS/miR-152 axis on ROCK1. Interestingly, ROCK1 was upregulated in OS cell lines   . These sThe regulatory mechanism of lncRNA in cells also affects the regulatory effect of miRNA, and the antagonistic or synergistic effect between the two together affects the expression of downstream genes . lncRNA miR-148/152 family is related to the regulation of metabolic activities and cell growth. The expressions of miR-148 and miR-152 are often coexpressed in cancer cells . miR-152In conclusion, the result showed that silencing the lncRNA HAGLROS by si-lncRNA HAGLROS significantly suppressed the proliferation and metastasis of OS cells. Mechanical studies showed that lncRNA HAGLROS could sponge miR-152, thereby suppressing the inhibition of the downstream ROCK1 gene and promoting the proliferation, invasion, and metastasis of OS cells. Our study provided new potential biomarkers for OS.


O-glycosylation as a novel PTM present on mouse OCN and occurring on a single serine (S8) independently of its carboxylation and endoproteolysis, two other PTMs regulating this hormone. We also show that O-glycosylation increases OCN half-life in plasma ex vivo and in the circulation in vivo. Remarkably, in human OCN (hOCN), the residue corresponding to S8 is a tyrosine (Y12), which is not O-glycosylated. Yet, the Y12S mutation is sufficient to O-glycosylate hOCN and to increase its half-life in plasma compared to wildtype hOCN. These findings reveal an important species difference in OCN regulation, which may explain why serum concentrations of OCN are higher in mouse than in human.Osteocalcin (OCN) is an osteoblast-derived hormone with pleiotropic physiological functions. Like many peptide hormones, OCN is subjected to post-translational modifications (PTMs) which control its activity. Here, we uncover  Bones provide support and protection for organs in the body. However, over the last 15 years researchers have discovered that bones also release chemicals known as hormones, which can travel to other parts of the body and cause an effect. The cells responsible for making bone, known as osteoblasts, produce a hormone called osteocalcin which communicates with a number of different organs, including the pancreas and brain.When osteocalcin reaches the pancreas, it promotes the release of another hormone called insulin which helps regulate the levels of sugar in the blood. Osteocalcin also travels to other organs such as muscle, where it helps to degrade fats and sugars that can be converted into energy. It also has beneficial effects on the brain, and has been shown to aid memory and reduce depression.Osteocalcin has largely been studied in mice where levels are five to ten times higher than in humans. But it is unclear why this difference exists or how it alters the role of osteocalcin in humans. To answer this question, Al Rifai et al. used a range of experimental techniques to compare the structure and activity of osteocalcin in mice and humans.The experiments showed that mouse osteocalcin has a group of sugars attached to its protein structure, which prevent the hormone from being degraded by an enzyme in the blood. Human osteocalcin has a slightly different protein sequence and is therefore unable to bind to this sugar group. As a result, the osteocalcin molecules in humans are less stable and cannot last as long in the blood. Al Rifai et al. showed that when human osteocalcin was modified so the sugar group could attach, the hormone was able to stick around for much longer and reach higher levels when added to blood in the laboratory.These findings show how osteocalcin differs between human and mice. Understanding this difference is important as the effects of osteocalcin mean this hormone can be used to treat diabetes and brain disorders. Furthermore, the results reveal how the stability of osteocalcin could be improved in humans, which could potentially enhance its therapeutic effect. Osteocalcin (OCN) is a peptide hormone secreted by osteoblasts, the bone forming cells . It reguWithin the bone tissue, OCN undergoes a series of post-translational modifications (PTM) that are critical for the regulation of its endocrine functions. Prior to its secretion, in the osteoblast endoplasmic reticulum, the OCN precursor (pro-OCN) is γ-carboxylated on three glutamic acid residues (Glu) by the vitamin K-dependent γ-glutamyl carboxylase . In the GPRC6A are associated with insulin resistance  composing mature mouse OCN are conserved in human OCN. This is in striking contrast with other peptide hormones involved in the control of energy metabolism such as leptin and insulin whose respective sequence display about 85% conservation between mouse and human. Second, the circulating concentrations of OCN, even though decreasing with age in both species, are five to ten times higher in mice than in humans throughout life span  see also. Based oO-glycosylation as a novel PTM presents in mouse OCN, and showed that this modification increases mouse OCN half-life in plasma ex vivo and in vivo. In contrast, mature human OCN does not contain the O-glycosylation site found in the mouse protein and consequently is not normally glycosylated. Yet, a single point mutation in human OCN is sufficient to elicit its O-glycosylation and to increase its half-life in plasma.Here, using proteomics and cell-based assays, we identified To better document the circulating level of OCN in humans and mice, we measured the serum concentration of OCN in wildtype mice at different ages (2 to 60 weeks) and compared the values with the reported serum level of OCN at corresponding life periods in humans . This anO-linked glycan adduct composed of one N-acetylgalactosamine , one galactose  and one or two N-acetylneuraminic acid (NANA). In addition to O-glycosylation, minor additional monoisotopic mass change corresponding to oxidation, unidentified modifications and/or adduct ions were also detected. Uncarboxylated OCN does not accumulate in bone ECM , a private chaperone required for the elongation of O-glycans , the enzymes responsible for initiating O-glycosylation, decreases the apparent molecular weight of OCN secreted in the medium  and three threonine (T) residues  is(are) residues , the twon occurs . As expen occurs . Further8 or T15 . Single eoblasts , a resulion site . TogetheO-glycosylation is initiated by the transfer of a GalNAc to a serine or threonine residue, a reaction taking place in the Golgi and catalyzed by GalNAc-Ts, a family of enzymes comprising 19 different members in mice  respectively, did not impact OCN 93 cells . Similarxylation . We alsoO-glycosylation is not regulating the processing of pro-OCN by furin or the secretion of mature OCN by osteoblasts. It was recently observed that O-glycosylation can also increase the stability of some peptide hormones in the circulation by preventing proteolytic degradation  was sufficient to significantly increase the expression of Ins1 as compared to vehicle or to 0.3 ng/ml non-glycosylated ucOCN stimulation. These difference in biological activity could be explained at least in part by an increased stability of glycosylated ucOCN as compared to non-glycosylated ucOCN in INS-1 832/3 cell cultures  in the human protein is sufficient to induce its O-glycosylation in osteoblasts as visualized by western blot  is sufficient to induce its O-glycosylation in osteoblasts.Sequence alignments revealed that the residue corresponding to S8 in the mouse protein is a tyrosine (Y12) in human OCN . In addiern blot . In contby furin . These rO-glycosylation impacts mouse ucOCN half-life in plasma, we next tested whether this PTM had a similar effect on human ucOCN. We produced and purified O-glycosylated human ucOCNY12S from HEK293 and compared its purity and molecular weight to native non-glycosylated human ucOCN by LC-MS, LC-MS/MS and SDS-PAGE (O-glycosylated (Bglap-/- mouse plasma at 37°C and the concentration of ucOCN monitored over time using a specific ELISA assay (O-glycosylated human ucOCN (see O-glycosylated version remains stable over the course of the experiment (i.e. 5 hr). As observed with the mouse protein, human ucOCN degradation was only inhibited when the plasma was heat-inactivated or incubated at 4°C  targeted by each of the GalNAc-Ts remain poorly characterized, although weak acceptor motifs for these have been identified by in vitro analyses  ructures . The inianalyses . Here, walNAc-T2 . In partalNAc-Ts . CombineO-glycosylation was shown to interfere with the action of proprotein convertases on several pro-hormones and receptors  and human (i.e. AYR↓R44) OCN at the corresponding position  residues at position 16 and 40, corresponding to R20 and R44 in human OCN, which could be potential cleavage sites for plasmin . A putatposition . Bovine  and R44 . Here we and R44 . Interes and R44 , an obse and R44 . Notably and R44 . Since tO-glycosylation protects mouse ucOCN from degradation in INS-1 cell cultures and consequently increases its biological activity. Interestingly, non-glycosylated ucOCN is stable when incubated in the INS-1 cell culture media at 37°C without cells (data not shown), suggesting that the proteolytic activity originates from the INS-1 cells and not from the culture media. The identity of a putative β-cell-derived protease responsible for ucOCN degradation remains unknown, since plasmin is expressed exclusively by the hepatocytes. Nevertheless, these results suggest that it may be more appropriate to use glycosylated ucOCN when studying the function of mouse OCN in cell culture.We also show that O-glycosylation and that consequently it has a reduced half-life in plasma ex vivo. The O-glycosylation sequence ‘SVPSPDP11’ of mouse OCN is conserved in the human protein, except for the amino acids corresponding to S5 and S8, which are replaced by a proline (P9) and a tyrosine (Y12) (i.e. ‘PVPYPDP15’). Remarkably, we could introduce O-glycosylation into human OCN by a single amino acid change (Y12S). O-glycosylated human OCN is protected from degradation in plasma ex vivo similarly to the glycosylated mouse protein. Hence, this difference in the O-glycosylation status of OCN could potentially explain why circulating level of OCN in 1- to 6-month-old mice is 5–10 times higher than the level measured in young or adult human  mice with BGLAP-Cre (Tg(BGLAP-cre)1Clem) transgenic mice that express Cre recombinase under the control of human OCN promoter as described previously (Bglap-/- mice (tm1Kry/Bglap/Bglap2tm1KryBglap/Bglap2) were generated using homologous recombination to replace OCN1 (Bglap1) and OCN2 (Bglap2) genes in the mouse Bglap cluster with a neomycin resistance cassette  pro-OCN mutant were purchased from Thermo Fisher. Human pre-pro-OCN cDNA cloned into pcDNA3 was purchased from GenScript. Each construct was used as PCR template for amplification and to introduce EcoRI and AgeI cloning sites and cloned in pIRES2-EGFP-V5 plasmid. Point mutations in mouse pro-OCN  and Y12S in human pro-OCN were generated by site-directed mutagenesis using specific primer .(Y12S) was generated using pIRES2-EGFP-hOCN (Y12S)-V5 as a template, to which a thrombin (Thr) cleavage site was added at the N-terminus and BglII and EcoRI restriction sites were introduced by standard PCR amplifications. The Thr-hOCN(Y12S) product was cloned in the pcDNA3.1-Fc-hinge-myc-His vector. The generated vector pcDNA3.1-Fc-hinge-Thr-hOCN(Y12S) is an expression vector of human OCN fusion protein composed of the Fc and hinge region of human IgG1, thrombin cleavage site and human OCN (Y12S). Mouse OCN fused to Fc were generated following the same procedure and using different primers .Primary osteoblasts were prepared following a previously described protocol . In brieN-acetylgalactosaminyltransferase inhibitor GalNAc-bn  or the proprotein convertase inhibitor Dec-RVKR-CMK , combined with 22 μM vitamin K1 (Sigma).Primary osteoblasts were transfected using jetPRIME Reagent (Polypus transfection). After an overnight incubation, media were changed to secretion media . After 24 hr of secretion, media were collected, and cells were lysed in protein lysis buffer  and analyzed by western blotting. In some experiments, osteoblasts were treated with the γ-carboxylation inhibitor warfarin , the 1. In some experiments, CHO-ldlD culture, transfection and secretion media was supplemented with 0.1 mM galactose and/or 1 mM N-acetylgalactosamine  to rescue the O-glycosylation defect as previously reported (Chinese hamster ovary (CHO) cells, originally purchased from ATCC, and Chinese hamster ovary ldlD cells  cells and GALNTs deficient HEK293 cells were generated using Zinc-finger nuclease (ZFN) gene editing as described previously , anti–β-actin , anti-GFP , anti-Gla-OCN goat antibody which recognize amino acids 11–26 of carboxylated mature OCN and anti-Cterm OCN goat antibody which recognize amino acids 26–46 of mature mouse OCN .O-glycosidase and 100 units of neuraminidases for 4 hr at 37 °C following the NEB kit protocol . Samples were resolved on 15% Tris-tricine SDS-PAGE gel and blotted using anti-Cterm OCN goat antibody.Flushed mouse femur and tibia from C57BL/6J were homogenized in lysis buffer . Tissue homogenates were then centrifuged for 10 min at 4000 rpm to remove insoluble material. In-vitro de-glycosylation assay was performed on 10 μg of bone homogenate. Briefly, proteins were denatured in denaturing buffer  at 95°C for 5 min and incubated with 80000 units of 4OH, snap frozen in liquid nitrogen and evaporated under vacuum using speedvac concentrator (Thermo scientific).Differentiated osteoblast secretion medium was spun down at 1500 rpm for 5 min to remove cells debris. The supernatant was then incubated overnight at 4°C with anti-Cterm OCN antibody in the presence of 1 × protease inhibitors cocktail. For bone extract, flushed femur and tibia from wild type mice were homogenized in lysis buffer containing . 100 μg of protein homogenate was diluted in 1,6 ml of 100 mM phosphate buffer pH 7.4 and incubated overnight at 4°C with anti-OCN antibody. After overnight incubation, samples were centrifuged at 10000 rpm for 10 min to remove precipitate and supernatant was incubated with protein-G agarose beads pre-washed with 1X PBS. After for 4 hr of rotation at 4°C, beads were spun down, washed twice with 1X PBS and three times with 50 mM Ammonium Bicarbonate pH 8.0. OCN was then eluted with 100 μl of 0.5 M NHhttps://doi.org/10.6084/m9.figshare.13259891.v1.Samples were diluted in 25% ACN 0.3%TFA and loaded onto a 50 × 4.6 mm PLRP-S 300A column (Agilent Technologies) connected to an Accela pump (Thermo Scientific) and a RTC autosampler . The buffers used for chromatography were 0.1% formic acid (buffer A) and 100% acetonitrile/0.1% formic acid (buffer B). Proteins and peptides were eluted with a two slopes gradient at a flowrate of 120 µL/min. Solvent B first increased from 12% to 50% in 4.5 min and then from 50% to 70% in 1.5 min. The HPLC system was coupled to a Q Exactive mass spectrometer or an Orbitrap Fusion (Thermo Scientific) through an Ion Max electrospray Ion Source equipped with a HESI-II probe and operated in positive ion mode. The spray and S-lens voltages were set to 3.6 kV and 60 V, respectively. Capillary temperature was set to 225°C. Full scan MS survey spectra (m/z 600–2000) in profile mode were acquired in the Orbitrap with a resolution of 70,000 or 120,000 with a target value at 3e6. The four most intense protein/peptide ions were fragmented in the HCD (higher-energy collision dissociation) collision cell upon collision with nitrogen gas and analyzed in the Orbitrap with a target value at 5e5 and a normalized collision energy at 33. The data acquisition software were Xcalibur 3.1 and Q Exactive 2.8 SP1 for the Q Exactive instrument and Xcalibur 4.0 and Tune 2.0 for the Orbitrap Fusion instrument. Data processing protocol: the identification of the different forms of OCN was performed by manual denovo sequencing using Qual Browser . The source files for the proteomics analyses can be downloaded through this link: Galnts gene copy numbers were calculated using the genomic DNA as a standard curve and variation between biological replicate was normalized using Actb expression level.RNA was extracted from non-differentiated and differentiated calvariae osteoblasts using Trizol reagent (Thermo Fisher Scientific) following standard protocol. RNA was treated with DNAse I and reverse transcribed using poly dT primers, random primers and MMLV reverse transcriptase (Thermo Fisher Scientific). QPCR was performed on standards of diluted genomic DNA and cDNA products using specific primers  on a ViiY12S and pcDNA3.1-Fc-hinge-Thr-OCN respectively using Lipofectamine 2000 reagent. Following 48 hr of transfection, cells were trypsinized and resuspended in sorting buffer containing . Cells were sorted at a concentration of 5–10 cells/well in 96 well plates containing the selection media EMEM, 10% FBS supplemented with G418 sulfate . Following two weeks of selection, isolated colonies appeared and the expression of mouse and human OCN was assessed using ELISA assay described below. Clones expressing the highest levels of OCN were amplified and frozen.To generate stable clonal cell lines expressing glycosylated human and mouse OCN, HEK293 were transfected with pcDNA3.1-Fc-hinge-Thr-hOCNY12S fusion protein were cultured in triple layer 175 cm2 flasks. After reaching 100% confluency, cells were kept in secretion media (EMEM media supplemented with 1% FBS and 10 μM warfarin to block γ-carboxylation) for 72 hr. Secretion media was collected, filtered with 0.45 μm filter, and media was pH buffered with 10 × binding buffer . This cell supernatant was then loaded into protein A affinity column  using liquid chromatography system (GE AKTA Prime Plus). Column was then washed with 20 ml of 1 × binding buffer  and 5 ml of filtered 1 × PBS. To release OCN from the column, OCN fusion protein was digested with thrombin  and eluted with 1 × PBS. Thrombin was subsequently removed using benzamidine sepharose . Mouse and human OCN purity were assessed using Coomassie staining and liquid chromatography-mass spectrometry (LC-MS) analysis compared to purified non-glycosylated mouse or human ucOCN. Mouse OCN was quantified using ELISA assay as described previously , an aliquot of plasma was diluted in ELISA assay buffer and kept on ice for OCN measurements. In some experiments, plasma was heat-inactivated for 30 min at 56°C, or treated with EDTA , phenylmethylsulfonyl fluoride , pepstatin A  which inhibits aspartic proteases , RVKR  or talabostat . Results are calculated in percentage relative to the initial concentration of non-glycosylated and glycosylated OCN respectively.The mouse OCN ex vivo half-life assays were performed with fresh plasma (lithium heparin) collected from four independent escribed , were inescribed . In thisescribed . TherefoBglap-/- mice was heat-inactivated for 30 min at 56°C, then diluted two times in the enzyme assay buffer containing 100 mM Tris buffer pH 7.5. Non-glycosylated OCN or glycosylated OCN was incubated at 50 ng/ml in the diluted plasma for two hours in the presence of different concentration of human plasmin (Sigma) ranging from 0 to 0.3 U/ml. After two hours, OCN level was measured using total mouse OCN ELISA. Results are represented as percentage of initial concentration of non-glycosylated and glycosylated OCN respectively.The plasmin enzymatic assay was performed on plasma ex vivo in the presence of non-glycosylated OCN and glycosylated OCN as follows: plasma from Bglap-/- male mice were injected intraperitoneally with 40 or 80 ng/g of body weight of mouse O-glycosylated ucOCN or non-glycosylated ucOCN, serum OCN level was analyzed at indicated time points using total mouse OCN ELISA. In all ex vivo and in vivo studies, mouse or human proteins were prepared in saline solution (0.9% NaCl) containing BSA (35 mg/ml) as a carrier.For in vivo half-life assay, 5 cells per well in 12-well plates and on day 5, cells were washed with PBS and incubated with culture media containing 5 mM D-glucose. Twenty-four hours later, cells were washed with PBS and incubated for 4 hr in culture media containing 1% FBS, 5 mM D-glucose and 0.1% BSA. OCN was added for 8 hr before supernatants and cells were harvested. ucOCN and O-gly ucOCN concentration were measured in supernatants by ELISA and cells were lyzed and RNA extracted as described in the material and methods section. Gene expression was analyzed using rat specific primers for Ins1 and Gapdh.The INS-1 832/3 rat insulinoma cell line (Millipore-Sigma) was cultured in RPMI-1640 with 11.1 mM D-glucose, supplemented with 2 mM L-glutamine, 1 mM sodium pyruvate, 10 mM HEPES, 0.05 mM β-mercaptoethanol and 10% FBS . On day t test was used, while one-way or two-way ANOVA followed by Bonferroni’s post-test were used for comparison of more than two groups. For repeated measurements , a repeated-measurement two-way ANOVA followed by Bonferroni’s post-test was used. A P value of less than 0.05 was considered statistically significant. All experiments were repeated at least three times or performed on at least three independent animals or included at least three independent biological replicates. In the in vivo half-life experiments, the Bglap-/- mice were randomized before the experiment into experimental groups such that the average body weight of each group was similar.Statistical analyses were performed using GraphPad Prism software version 7.03. Results are shown as the mean ± SEM. For single measurements, an unpaired, 2-tailed Student’s  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:O-glycosylation as a novel posttranslational modification, but also documents species specificity. Notably, mouse osteocalcin is O-glycosylated at serine 8, whereas human osteocalcin is not. Review comments have been thoughtfully and thoroughly addressed.The study not only highlights the importance of Decision letter after peer review:O-glycosylation in mice, but not in humans" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Mone Zaidi as the Reviewing Editor and Clifford Rosen as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: T John Martin (Reviewer #1).Thank you for submitting your article "The half-life of the bone-derived hormone osteocalcin is regulated through The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before we consider it for publication, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:O-glycosylated at Ser-8, independently of processing and γ-carboxylation. Human osteocalcin was found not to be O-glycosylated, but mutation to Ser-8 allowed that process to occur. If increased stability of osteocalcin in vitro as a result of O-glycosylation is real, it could be of interest. The paper was found to be well written with figures illustrating the findings. With that said, several key experiments are required that would considerably strengthen the conclusions. Notably, without information on the biological outcomes of O-glycosylation, the paper is seen to be of limited interest.The work establishes that mouse osteocalcin is Essential revisions:O-glycosylation at Ser-8 is measured identically in this ELISA. The authors should specify what capture antibodies were used. This query applies also to data with human OCN and the Ser-8 mutant that is O-glycosylated. Furthermore, does the commercial ELISA kit measure glycosylated mutant and normal hOCN as identical?1) Regarding the ELISA , capture antibodies can distinguish between carboxylated and non-carboxylated OCN. For the present work, it is essential that the authors show that OCN with or without O-glycosylated vs. wt ucOCN should therefore be compared using timed estimations during the declining phase.2) Figure 3H-J show a statistically significant difference in levels of glycosylated and non-glycosylated mouse OCN. These experiments however do not measure "half-life" as claimed in the title and Abstract. The in vivo half-life of injected O-glycosylated, whereas the mouse OCN is. The biological significance of this difference in O-glycosylation thus needs to be established. While knocking-in a mutation to abolish O-glycosylation will provide the most definitive answer, the reviewers consider this not to be feasible during the pandemic. Therefore, at the very least, a cell-based assay should be used to compare biological activity. Examples are the dose-dependent increase in insulin mRNA in mouse pancreatic islets . Very low dose ucOCN was shown in that work to promote insulin expression in islets in a dose-dependent manner. An alternative approach, arising from the same paper, would be to show at slightly higher doses, a dose-dependent increase in adiponectin expression in mouse adipocytes. The authors might have other possibilities of cell- based assay.3) A feature of OCN that interested the authors was the remarkable difference in circulating amounts – more than 10 times higher in the mouse. This work appears to be part of a search for mechanisms to explain this, although they might consider that these are evolutionary changes, including the fact that there is only 65 % conservation of sequence between mouse and human , and the human OCN is not  Essential revisions:1) Regarding the ELISA , capture antibodies can distinguish between carboxylated and non-carboxylated OCN. For the present work, it is essential that the authors show that OCN with or without O-glycosylation at Ser-8 is measured identically in this ELISA.O-glycosylated non-carboxylated mouse OCN (O-gly ucOCN). It shows that our total mouse osteocalcin ELISA assay reacts equally with O-glycosylated and non-glycosylated mouse OCN at concentrations ranging from 12.5 to 50 ng/ml. However, the assay slightly underestimates the O-gly ucOCN at 100 ng/ml compared to non-glycosylated OCN. For this reason, in all our experiments, samples were diluted at concentration of less than 50 ng/ml prior to be measured in the ELISA. This is now clearly stated in the Materials and methods section, and in the Results section.The reviewers raised an important point. We now show in Figure 3—figure supplement 2 a standard curve of non-glycosylated non-carboxylated mouse OCN (ucOCN) ran side-by-side with a standard curve of The authors should specify what capture antibodies were used.As detailed in the original paper describing this ELISA assay , the capture antibody is a goat polyclonal antibody directed against the central part (amino acids 11 to 26) of mouse OCN (anti-MID OCN) and recognizing with equal affinity the uncarboxylated and carboxylated OCN proteins. The detection antibody is a goat polyclonal antibody directed against the C-terminal region (amino acids 26 to 46) of mouse OCN (anti-CTERM OCN). Of note, the glycosylation site (Ser 8) is not comprised in these two epitopes, most likely explaining why the ELISA recognizes both forms. The information on the capture and detection antibodies used has now been added to the Materials and methods section.This query applies also to data with human OCN and the Ser-8 mutant that is O-glycosylated.As detailed in the publication reporting the human ucOCN ELISA , the capture antibody in this assay is a mouse monoclonal antibody (8H4) specific to the C-terminal region of human OCN . The detection antibody is a mouse monoclonal antibody (4B6) specific to the mid-region of human ucOCN . This information has now been added to the Materials and methods section.Furthermore, does the commercial ELISA kit measure glycosylated mutant and normal hOCN as identical?O-glycosylated non-carboxylated human OCN (O-gly uc-hOCN). This shows that the human uncarboxylated OCN ELISA assay recognizes equally glycosylated and non-glycosylated human OCN at all the concentrations tested . This is now stated in the Results section, and in the Materials and methods section.To answer this valid question, we now show in Figure 5—figure supplement 2 the standard curve of non-glycosylated non-carboxylated human OCN (uc-hOCN) ran side-by-side with a standard curve of 2) Figure 3H-J show a statistically significant difference in levels of glycosylated and non-glycosylated mouse OCN. These experiments however do not measure "half-life" as claimed in the title and Abstract. The in vivo half-life of injected O-glycosylated vs. wt ucOCN should therefore be compared using timed estimations during the declining phase.To answer this point, we reanalyzed our data on OCN in vivo stability. As suggested by the reviewer, we calculated the % of OCN in serum relative to the peak at 30 minutes. This new result, now presented in Figure 3I, shows that glycosylated ucOCN has a significantly longer half-life in circulation compared to non-glycosylated form in fasting conditions . These new data are presented in Figure 3I and mentioned in the text on page 13, line 277.3) A feature of OCN that interested the authors was the remarkable difference in circulating amounts – more than 10 times higher in the mouse. This work appears to be part of a search for mechanisms to explain this, although they might consider that these are evolutionary changes, including the fact that there is only 65 % conservation of sequence between mouse and human , and the human OCN is not O-glycosylated, whereas the mouse OCN is. The biological significance of this difference in O-glycosylation thus needs to be established. While knocking-in a mutation to abolish O-glycosylation will provide the most definitive answer, the reviewers consider this not to be feasible during the pandemic. Therefore, at the very least, a cell-based assay should be used to compare biological activity. Examples are the dose-dependent increase in insulin mRNA in mouse pancreatic islets . Very low dose ucOCN was shown in that work to promote insulin expression in islets in a dose-dependent manner. An alternative approach, arising from the same paper, would be to show at slightly higher doses, a dose-dependent increase in adiponectin expression in mouse adipocytes. The authors might have other possibilities of cell- based assay.O-gly ucOCN (0.3 and 0.1 ng/ml) can increase the expression of the insulin encoding gene, Ins1, suggesting that O-gly ucOCN is biologically active. Moreover, although 0.3 ng/ml of non-glycosylated ucOCN was also able to increase Ins1 expression by ~1.5-fold, we noted that the O-gly ucOCN had a stronger effect at similar or even lower doses . This difference in the biological activity in culture could be explained at least in part by the observation that O-gly ucOCN is more stable than ucOCN when incubated in INS-1 832/3 cell cultures . Overall, these new data support the notion that O-gly ucOCN is biologically active, at least in a cell-based assay, and that O-glycosylation also increases the stability of ucOCN in cell culture. These new results are included in Figure 4A and B, and verbalized in the subsection “Impact of glycosylation on OCN activity in culture”. They are also discussed in the Discussion section.We thank the reviewers for this suggestion. To address it, we have established a cell-based assay using the INS-1 832/3 rat insulinoma cell line, which respond well to natural incretin hormones such as glucagon-like peptide 1 and gastric inhibitory peptide . In the condition of this assay, we found that, compared to vehicle, low doses of mouse


To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel “modality-agnostic training” technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of  It is a crucial step for pre-processing neuro-imaging datasets and has an immediate bearing on all subsequent investigative procedures. It is also a necessary processing step in most studies for compliance with privacy-preserving regulations, such as the Health Insurance Portability and Accountability Act of 1996 (HIPPA) and the General Data Protection Regulation of 2016 (GDPR). The effects of brain extraction on various analyses have been reported in the literature, including brain tumor segmentation , cerebraManual delineation of the brain  is very Importantly, most existing computational methods for brain extraction were developed for and evaluated on brain scans without apparent pathologically-affected regions such as tumors or lesions, and they typically use only the T1-weighted MRI scans. Scans with brain tumors may present an important challenge for supervised machine learning algorithms that exclusively train on scans of healthy subjects, causing them to fail, particularly in the proximity of the tumor regions. Suboptimal brain extraction on these scans may result in parts of the skull and neck, which usually have tumor-like radiographic characteristics, being included on the final brain mask, or tumor regions being considered as non-brain, hence in both cases adversely affecting consecutive analyses. Example snapshots of such problematic brain extraction using the brain extraction tool (BET)  are showAnother important challenge of brain extraction on multi-institutional, multi-parametric MRI (mpMRI) scans with tumors is the complexity and variability of the imaging data. Brain tumor scans acquired during standard clinical practice or even research studies and clinical trials, include a lot of sources of variation affecting their intrinsic radiographic characteristics/appearance, which may not be perceivable by visual observation but even the sub-visual differences affect further computational analyses. Examples of sources of variation include, but are not limited to imaging modalities, quality of the scans, scan acquisition parameters, e.g., repetition time (TR), echo time (TE), flip angle, slice thickness. Hence, robustness of algorithms to imaging variations and their generalizability to available imaging modalities are important requirements for achieving widespread application in clinical and research settings.Finally, a third important requirement for the field is to have tools that are easy to deploy and apply, and with a low computational footprint. In methodological research studies, obtaining the highest performance is often the main goal, undermining other concerns such as complexity, running time and portability of a new method. However, if the goal is clinical translation and widespread usage, methods should be designed for being used by a variety of users with different hardware and software requirements, as well as competency levels.In this study, we sought to provide a tool that addresses the specific challenges of brain extraction on scans with brain tumors, with a particular focus on coming up with a practical approach that is applicable and generalizable across multiple institutions and multiple modalities, further facilitating collaborations. Towards this aim, we performed a comprehensive performance evaluation of recently established DL architectures for semantic segmentation. We evaluated 5 widely used DL network architectures, using multiple datasets and various cross-validation strategies. Evaluation data included mpMRI brain tumor scans acquired during standard clinical practice under varying acquisition protocols from 3 private institutions, as well as publicly available data from The Cancer Imaging Archive (TCIA) , 2017c, 2.2.1.mpMRI scans are routinely acquired across institutions for patients with suspected brain tumors. Scanning protocols, i.e., scanner models, acquired modalities, and scan parameters, vary across institutions, as well as depending on the time of scan with respect to patient’s treatment history. In this study, we included four structural modalities that are commonly acquired at baseline pre-operative time-point: native (T1) and post-contrast T1-weighted (T1Gd), native T2-weighted (T2), and T2-weighted Fluid Attenuated Inversion Recovery (Flair) MRI scans.n = 1; 812), b) Thomas Jefferson University , c) MD Anderson Cancer Center . The public data are available through TCIA (n = 328) (itksnap.org) software  the University of Pennsylvania   and b) Tn = 328) , with thn = 328) , 2018. Nn = 328) . The grosoftware  and apprImportantly, this multi-institutional data collection was highly heterogeneous with a wide range of variability in scan quality, slice thickness of different modalities, scanning parameters, as well as pre-processing of initial scans, e.g . BrieflyExamples of scans for each institution are shown in 2.2.2 pre-processing protocol of the BraTS challenge  to anisotropic (1:875× 1:875× 1:25mm3). These scans were finally used for training all the DL architectures. An overview of the pre-processing applied in this study is shown in Since the scans included in this study were heterogeneously obtained from different scanners and acquisition protocols, they all underwent the same pre-processing protocol to make image dimensions and voxel sizes uniform across studies and modalities. Specifically, all DICOM scans were converted to the NIfTI  file formely SRI . The remlgorithm  was used2.3.Multiple DL network architectures were tested in our comparative performance evaluation. Our selection was mainly motivated by their wide application on other segmentation tasks. The specific architectures included here comprise a) 2D U-Net , b) 3D U2.3.1.U-Net  was orig2.3.2.The 2D U-Net was extended in 3D volumetric variations in 2016 . Here we utilize a 3D U-Net  with a customized image input size of 128 128 × 128 × 128. Furthermore, to accommodate hardware limitations, we use 16 base filters, compared to the original implementation that included 64. The pattern for a single convolution block was given as 3D Convolution operation (3 × 3 × 3) followed by Leaky ReLU (LReLU) and Instance Normalization with running statistics during learning. Adam optimizer was used with a learning rate of 0.01 over 25 epochs. The number of epochs was determined according to the amount of improvement observed. Notably, we evaluated the basic 3D-U-Net architecture in terms of the initial numbers of filters and their relation to the segmentation quality. Details of this analysis, showing the trade off between hardware requirements and performance, can be found in the 2.3.3.We extended 3D U-Net, by applying residual connections improving the backpropagation process . Our 3D-Res-U-Net implementation follows the principle of the 3D U-Net architecture, while further including skip connections in every convolution block, differently from other methods that use a 3D UNet. A schematic that illustrates these operations is shown in 2.3.4.The FCN architecture , which was introduced in 2017, captures hierarchical features, imaging patterns, and spatial information of each input image with receptive fields. FCN is considered less computationally expensive than other architectures, due to not having an “expensive” decoding part as U-Net, and hence provides faster coarse segmentation of various problems . Here, w2.3.5.3 is a 3D CNN with a depth of 11-layers, and a double pathway to provide sufficient context and detail in resolution. In our evaluation, we applied the original version of DeepMedic4 with the default parameters provided, and we applied a hole-filling algorithm as a post-processing step.DeepMedic  was originally proposed in 2015, as the first 3D architecture, when it emerged as a winner of the ISchemic LEsion Segmentation (ISLES) challenge . The sta2.4.Considering the complexity of the task, i.e., the combinatorial explosion of experiments using multiple sites, train/test combinations, input modalities and network architectures, we followed a multi-step experimental design, focusing on each step on a specific objective, as described below.All hyper-parameters stayed consistent throughout all the experiments. Each of the applied architectures needed different time for convergence, based on their individual parameters. 2.4.1.On this first step of our evaluations, we performed an exhaustive comparison of various network architectures and input image combinations. Specifically, models of all architectures were trained solely on data from a single institution  and then inferred on datasets of multiple institutions . During the training phase, a hold-out cohort of 180 mpMRI scans from 45 UPenn subjects were used for internal algorithmic validation and convergence evaluation. Each method was trained using as input either single individual scans or combined mpMRI scans, as described in In our attempt to get a more complete picture, we have also compared these DL architectures with BET and FreeSurfer, which can be considered two of the most widely used traditional approaches for brain extraction. For this comparison we only utilized the T1 scans that BET and FreeSurfer can utilize. Furthermore, to showcase how the disease can affects the performance of DL algorithms we have trained a DL model on scans from 50 healthy subjects (2D-Res-Inc-H) acquired for different studies and applied it in the same brain tumor scans we used for this step of the evaluation. The ground truth labels of the healthy brain scans were generated from different raters and confirmed for obvious errors by an expert data scientist with experience working in neuroimage analysis for more than 9 years.2.4.2.On the second step, we evaluated the performance of a “modality-agnostic training technique”, which we developed aiming to provide a tool that is widely applicable to datasets with different MRI modality combinations. A similar concept was also recently presented by , which s2.4.3.The size and diversity of the training dataset are major factors affecting the performance and generalizability of DL models. In order to evaluate the effect of multi-site training on segmentation accuracy we performed additional experiments using models trained exclusively on data from a single institution (UPenn only) against models trained on multi-site data, i.e., UPenn, TJU, and MDA. For both cases, we used the large multi-institutional TCIA dataset for testing, thus applying the final models on a highly heterogeneous and previously unseen dataset, which allowed us to evaluate the generalizability of the proposed DL models.2.5.2.5.1.Following the current literature on brain extraction methodologies, where T1 is solely used, we trained every architecture on T1. Additionally we trained models on each other available individual MRI modality, to compare the potential contribution of each modality beyond T1. In the remainder of this manuscript we refer to these models using the notation “modality-modality” to indicate that a single modality was used for both training and inference. For example we refer to a 3D U-Net trained and inferred on T1, as T1-T1 3D-U-Net.We should note that the performance of models trained on individual modalities was also evaluated in ensemble configurations.2.5.2.To take advantage of the richness of the routine mpMRI acquisitions for brain tumor patients, we also considered training methods on multiple modalities, instead of training on single modality individually. We limited these experiments to two main configurations: a) combining T1 and T2, that we refer to as “Multi-2”, and b) combining all 4 structural mpMRI modalities together that refer to as “Multi-4”. The first configuration (Multi-2) is selected as it was previously shown that T2 contributes to improved skull-stripping performance  and can 2.5.3.We propose a novel approach that provides a “hybrid” alternative to single-modality and multi-modality training techniques, aiming to optimally use multi-modal imaging data, while not necessarily requiring existence of a pre-defined set of modalities. Specifically, in this configuration, a DL model is trained using all available modalities for a subject. However, in contrast to the multi-modality approach where the combination of all modalities from a subject is used as a single data sample, we feed each modality to the network as an independent data sample. This process attempts to contribute on forcing the network to learn the brain shape prior, instead of texture priors that CNNs usually learn . In this way, the model allows making an inference from a single modality, while being trained on multi-modal data. Importantly, during the inference, the model does not need to know which modality was provided as the input scan. The most important practical benefit of this approach is its robustness to missing data, i.e., it can be applied directly even if a specific modality is missing or is not usable due to low scan quality.2.6.Following the literature on semantic segmentation we use the following metrics to quantitatively evaluate the performance of the trained methods. We have further utilized the metrics of sensitivity and specificity in the 2.6.1.Dice) is commonly used to evaluate and report on the performance of semantic segmentation. Dice measures the extent of spatial overlap, while taking into account the intersection between the predicted masks (PM) and the provided ground truth (GT), hence handles over- and under-segmentation. Dice can be mathematically defined as:The Sørensen-Dice Similarity Coefficient h better than DeepMedic in all datasets and models with the exceptions of i) the cases where 3D-ResU-Net improvements are not significant , ii) “T1Gd-T1Gd” on MDA data where DeepMedic is insignificantly better, and iii) “T2-T2” and “Flair-Flair” models where DeepMedic shows significantly (p < 0:0035) improved performance. When comparing 3D-ResU-Net with 2D-ResInc, we note that 3D-ResU-Net is comparable but significantly (p < 0:033) worse than 2D-ResInc for all datasets for all models except, i) “T2-T2” where 2D-ResInc fails dramatically and 3D-ResU-Net is significantly (p < 1:45E−05) better, and ii) “Flair-Flair” and “Ensemble” models, where 2D-ResInc is better than 3D-ResU-Net, but with insignificant differences. 3D-ResU-Net is also significantly (p < 2:56E−06) better than FCN for all datasets for all models, and when compared to 3D-UNet, 3D-ResU-Net is significantly (p < 0:0025) better for all datasets for all models, except “Multi-4” for the UPenn data, where still 3D-ResU-Net is better than 3D-UNet but with insignificant differences.We have further conducted a statistical analysis using Wilcoxon tests, where we compared the Dice between them when compared to the other architectures. Since a major goal of our analysis is also the practicality and footprint of the method both during training and inference, we considered the relatively simpler 3D-Res-U-Net and DeepMedic architectures over the 2D-ResInc for the follow up evaluations.Based on the quantitative results of these experiments, we conclude that the 3D-Res-U-Net, DeepMedic, and 2D-ResInc are the three best performing approaches and perform comparably well. The statistical analysis indicated that 2D-ResInc is outperforming 3D-ResU-Net in most cases with the exception of the “T2-T2” model where 2D-ResInc fails dramatically. Furthermore, between 3D-Res-U-Net and DeepMedic we note that 3D-Res-U-Net is better in most models, with consistently the smallest differences in 3.3.Comparative evaluations involved the 3D-Res-U-Net and the Deep-Medic models trained using the modality-agnostic technique, compared against the 3D-Res-U-Net and the DeepMedic using the best performing input combinations in the previous step, i.e., “T1-T1”, “Multi-2”, and “Multi-4” models. Similar to step 1, these models were trained on the same training dataset from UPenn and inference and evaluation was performed using the same three testing datasets. Note that the modality-agnostic model was only trained once using all single modalities sequentially as input. The model was then applied on each modality independently, and the performance was evaluated for each modality separately.Comparative results for different models are shown in 3.4.We further investigated the effects of training on multi-site data using the combination of UPenn, TJU, and MDA as our training dataset. Trained “T1-T1” and the modality agnostic T1 (“M-A-T1”) models were then evaluated on the independent multi-institutional TCIA dataset . We obse4.https://github.com/CBICA/BrainMaGe.In this study, we conducted a comprehensive performance evaluation of widely-used DL methods for the task of brain extraction. We selected multiple recent architectures established in the domain of 3D semantic segmentation, while also considering low computational footprint and out-of-the-box implementation availability. We further introduced a novel modality agnostic training technique, preferable to models trained using individual MRI modalities, considering the benefits of its flexibility to availability of different input image modality combinations. The accompanying source code can be found at: Many automated methods have previously been developed for brain extraction . In receDice score as Thr < 80%. Similarly, good segmentations were randomly chosen using a threshold on Dice score as Thr > 98%.We assessed a total of 7 modality configurations, including both independent MRI scans and combinations of mpMRI brain tumor scans. Amongst these configurations, our results are in line with the existing literature on brain extraction, where the T1-weighted scan is typically used for brain extraction. These results indicate that utilization of the T1 modality (“T1-T1”) achieves the best performance also for scans with brain tumors. However, we should also note that ground-truth labels are generally delineated using the T1 scans, thus creating a potential bias. Ensemble segmentation via majority voting did not contribute in improving performance. The only other model showing comparable performance was the one trained on all 4 modalities, i.e., “Multi-4”. However, between these two alternatives, the “T1-T1” model would be preferred owing to the requirement of only one modality, T1, instead of requiring the co-existence of four modalities to train the “Multi-4” model. Poor and good illustrative examples are shown in We introduced an alternative novel modality-agnostic brain extraction approach, which is not dependent on any particular modality and may be trained on any available modality. There are at least two reasons why training the algorithm and running it on a limited number of modalities is essential in clinical applications. First, some clinical MRI brain protocols do not include all four modalities. For example, at our institution, the GammaKnife protocol consists only of T2-FLAIR and T1-Gd scans, and hence skull stripping would have to be performed using at most two modalities. The other reason pertains to processing speed during inference, where brain extraction and further analysis has to be performed real time . Therefore the time needed for pre- and post-processing is directly affecting the radiological reading, as the more modalities included for processing, the more time it will take to perform the final task and also the more error prone the complete pipeline will be. The results of models trained on data from a single institution and applied on data of other independent institutions  yield a consistently comparable performance between the proposed modality-agnostic approach and the “T1-T1” and “Multi-4” models. To rigorously validate the proposed modality-agnostic method on diverse multi-institutional data, we chose to first train two different models using i) data from a single institution (UPenn) and ii) multi-institutional data (UPenn + TJU + MDA), and then infer them on multi-institutional diverse data from TCIA, i.e., from institutions not included in the training cohort. The results  indicateWhile aiming on a practical brain extraction tool, generalizable across multiple institutions and multiple modalities, to facilitate the current paradigm for multi-institutional collaborations through data sharing, the current study has three major limitations: a) did not use any defaced/semi-skull-stripped data, b) consideration of only pre-operative scans, and c) consideration of only brain glioblastomas. Specifically, our models were trained on images prior to any defacing, which is a common practice mandated by many institutions for data sharing purposes, in order to comply with privacy regulations. We therefore identified another independent retrospective cohort of 120 scans from Washington University in St. Louis (WashU) that have followed the common practice of data sharing after defacing. We attempted to evaluate our current models on defaced data and we noted an inferior performance –12 when 5.Brain extraction is a key component of computer algorithms that are poised to change the practice of clinical neuroradiology. It may facilitate real time automatic brain lesion detection and segmentation for clinical interpretation. It is therefore critical that this component be not only fast, but also have the flexibility to accommodate different MRI protocols, even with a limited number of sequences, and different hardware platforms. Our study demonstrates that DL brain extraction algorithms have the potential to satisfy those requirements.https://github.com/CBICA/BrainMaGe.In this study, using extensive validation protocols, we have shown that DL methods generally out-perform conventional brain extraction methods, such as BET, and FreeSurfer. Though accuracy is important, practical constraints dictate that the importance of the trained model’s generalizability. Towards this end we introduced a modality-agnostic training method rather than one that needs a specific set or combination of modalities, which forces the model to learn the spatial relationships between the structures in the brain and its shape, as opposed to texture, and thereby overriding the need for a particular modality. In addition, we have also proved that such a model will have comparable (and in most cases better) accuracy to other DL methods while keeping minimal computational and logistical requirements. Finally, we have publicly released the accompanying source code at: 1


Attention deficit hyperactivity disorder (ADHD) is a common neuropsychiatric disorder characterized by inattention, hyperactivity, and impulsivity. It may be accompanied by learning difficulties and working memory deficits. Few studies have examined the role of miRNAs in cognitive dysfunction in ADHD. This study investigated the effects of aberrant miR-384-5p expression on learning and memory in a widely used ADHD rat model. Lentiviral vectors were injected into the lateral ventricles of the rats to increase or decrease miR-384-5p level. To determine whether aberrant miR-384-5p expression affects learning and memory, spontaneous activity and cognitive function were assessed with the open field and Morris water maze tests. In the place navigation experiment of the Morris water maze test, time, and total swimming distance to reach the platform decreased compared to the control group when miR-384-5p was overexpressed, whereas down-regulation of miR-384-5p had the opposite effect. There were no obvious changes in brain tissue morphology following miR-384-5p overexpression or inhibition; however, dopamine (DA) receptor D1 (DRD1) level has decreased and increased, respectively, in the prefrontal cortex (PFC). The luciferase activity of the wild-type DRD1 group has decreased in luciferase reporter assay. Cyclic AMP response element-binding protein (CREB) phosphorylation has increased, and DA transporter (DAT) level has decreased in the PFC of spontaneously hypertensive rats (SHR) by miR-384-5p overexpression. On the other hand, miR-384-5p suppression increased DRD1 and decreased DAT and CREB protein levels relative to control rats. These findings suggest that miR-384-5p may play a critical role in learning and memory impairment in ADHD. Attention deficit hyperactivity disorder (ADHD) is a common neuropsychiatric disorder that begins in childhood  and affeThe dopaminergic neurotransmitter system plays a critical role in ADHD . DopaminMicroRNAs (miRNA) are non-coding small (19- to 24-nucleotide) RNAs that regulate gene expression post-transcriptionally by inhibiting mRNA translation or inducing mRNA degradation, although some miRNAs have a positive regulatory function. In the brain, miRNAs modulate cellular functions and participate in cell fate specification, neuronal survival, neurite projection, and synaptic plasticity . Around The miR-384-5p is highly expressed in the rat hippocampus (Hip)  and is eMale SHR and Wistar Kyoto (WKY) rats (4 weeks old) were obtained from supplier Charles River, Beijing Vital River Laboratory Animal Technology Co. , and were maintained in a controlled environment at 23 ± 1°C and a relative humidity of 50 ± 5% on a 12/12-h light/dark cycle. All animals had free access to food and water . The res8 TU/ml), constructed by Hanbio Biotechnology Co. , were injected into the lateral ventricles   by intraperitoneal injection. The depth of anesthesia was determined by monitoring reflexes, heart rate, skin color, and respiratory rate. Anesthetized rats were placed on a stereotactic instrument and intracerebral injection was performed using a syringe with 10 μl volume and 0.5-mm flat needle. Lentiviral vectors  . The inf−3.0 mm) .For the open field test, we used a black box with a height of 45 cm and a bottom of 40 × 40 cm, with a 4-W cold light source above the center of the box. Animal activity was recorded using Shanghai Jiliang Animal Behavior Analysis System . Behavioral tests were performed 1 week after lentivirus injection. Rats were placed in the center of the open field and their activity under light conditions was recorded by an infrared camera system and video synthesizer. Movement trajectories were analyzed by a computer analysis system. Analyzed behavioral changes included total crawling distance, number of rearings, and activity. After each experiment, the interior of the box was cleaned with 75% alcohol solution to remove the odor of the rat so that it would not affect the behavior of the next rat.For the Morris water maze test, a water pool with a diameter of 160 cm and a height of 50 cm was used. Water depth was 31 cm, and the temperature was maintained at 23 ± 2°C. The pool was divided into four equal-sized quadrants, each with a uniquely colored pattern on the wall as a reference. A circular platform with a diameter of 9 cm and a height of 30 cm was placed in the center of the target quadrant, submerged 1 cm below the water surface. Rats were placed in the pool on the day prior to the navigation test and were allowed to swim for 2 min to acclimatize to the maze. In each trial, the rats were introduced into the maze facing the wall at one of the four designated starting points. A camera connected to a computer was placed above the maze to record swimming trajectory, total swimming distance, and time spent searching for the platform. If the rats did not find the platform within 120 s, the latency was recorded as 120 s. Following the test, these rats were placed on the platform for 15 s to allow learning and retention. Each rat was tested four times per day, each with a different starting point. There was a 30-min rest period between trials. The experiment lasted 5 days, and the average latency and total distance were recorded and analyzed using the Shanghai Jiliang Animal Behavior Analysis System. Twenty-four hours after the last place navigation test, the platform was removed and the rats were allowed to swim freely for 120 s, once for each animal. The number of target crossings in 120 s, and time and distance in target quadrant were calculated for the spatial probe test.The animals were anesthetized with 10% chloral hydrate after behavioral tests, and the fresh brain tissue was removed. Brains were fixed overnight by immersion in 4% paraformaldehyde. After gradient dehydration with ethanol and dimethyl benzene, the brains were embedded in paraffin. Sections 5 μm thick in coronal position at optic chiasm were made and mounted on the slides. Slides were deparaffinized and hydrated in water. They were then immersed in hematoxylin solution for 3–5 min and then rinsed in water. Following this, slides were differentiated with acid alcohol and then rinsed again. After immersing the sections in ammonia solution, they were washed in slowly running tap water. Slides were then dyed in eosin solution for 5 min, dehydrated, and sealed. Cellular morphological changes were observed under an optical microscope. Neurons were assessed as necrotic when they showed nuclear condensation, nuclear breakdown, nuclear dissolution, and cytoplasmic acidophilia. The arrangement of cells in brain regions was observed. Cell counting was manually performed in the hippocampus , prefrontal cortex, and corpus striatum. We quantified three serial sections per brain region from each animal (four rats in each group) and then calculated the average.−ΔΔCT method. The fold change was calculated using the expression relative to controls.Rats were anesthetized with chloral hydrate and decapitated. Unilateral tissue samples were taken for the molecular experiments. The hippocampus (Hip), prefrontal cortex (PFC), and corpus striatum (Str.) were removed and total RNA was extracted with the miRNeasy Mini Kit ; 1 μg of RNA was reverse transcribed using the PrimeScript RT reagent Kit  to generate cDNA. Bulge-loop™ U6 and miR-384-5p qRT-PCR Primer Sets (one for reverse transcription and its pair for amplifying each target gene) were synthesized by RiboBio . The forward and reverse primers for target genes were as follows: β-actin, 5′-CCTAGACTTCGAGCAAGAGA-3′ and 5′-GGAAGGAAGGCTGGAAGA-3′; DRD1, 5′-TCAAGAGGGAGACGAAAGT-3′ and 5′-ACAGAAGGGCACCATACA-3′. U6 and β-actin were used as internal controls for miR-384-5p and DRD1, respectively. The PCR experiments were run in triplicate (test–retest reliability was 0.873–0.982), and the CT value ranged between 15 and 35. The ViiA7 system  was used for qRT-PCR, and fold difference in transcript expression level was calculated with the 2Tissues were lysed in radioimmunoprecipitation assay buffer  with protease and phosphatase inhibitors. Protein concentration was determined with a bicinchoninic acid protein assay kit (Beyotime Institute of Biotechnology). Proteins were separated on a 12% sodium dodecyl sulfate–polyacrylamide gel and transferred to a polyvinylidene difluoride membrane  that was blocked and incubated overnight at 4°C with primary antibodies against β-actin , DAT , and DRD1 , CREB , and phosphorylated (p-)CREB  . The membrane was then incubated with appropriate horseradish peroxidase-conjugated secondary antibodies . Page ruler pre-stained protein ladder  suitable for approximate molecular weight determination was also used. Band intensity was measured using a Luminescent image analyzer AlphaView SA . For quantitative analysis, total proteins were used as loading controls for phosphoprotein, and the fold change was calculated using the expression in up- or down-regulation following miR-384-5p administration compared to controls.DRD1 gene was amplified by PCR and cloned into the pmiR-RB-REPORT™ luciferase reporter vector (RiboBio). HEK293 cells were cultured in Dulbecco's Modified Eagle's Medium supplemented with 10% fetal bovine serum, 4 mM l-glutamine, and 100 mg/ml Normocin . The cells were co-transfected with the luciferase reporter vector containing wild-type or mutant DRD1 3′ UTR and miR-384-5p or control plasmid using Lipofectamine 2000 . At 48 h after transfection, relative luciferase activity was measured with the Dual Luciferase Reporter Assay Kit  and the GloMax luminometer (Promega). Results are expressed as the intensity ratio of Renilla to firefly luciferase. The experiment was performed in triplicate and repeated three times.The 3′ untranslated (UTR) region of the t-test and non-parametric Mann–Whitney U-test were used. All data are presented as means ± standard deviation (SD) or median and interquartile range for parametric or non-parametric data, respectively. For all analyses, significance was assigned when P < 0.05.All data were inspected for normality to ensure their appropriateness for statistical analyses. According to the analyzed variables, Student's two-tailed n = 5, median (interquartile range) values were: PFC: 0.08 , Str: 0.59 , Hip: 0.09 ± 0.03] compared to WKY rats  , Str , and Hip  or empty vector control  delivered by lentivirus injection into the cerebroventricle of SHR rats at 4 weeks of age. We found that relative miR-384-5p expression was lower in the PFC , Str , and Hip  of the miR-SH group compared to the control NC-SH group (SHR rats were randomly divided into two groups that were administered with miR-384-5p overexpression vector virus (miR-OV group) or virus harboring an empty vector (NC group) by intracerebroventricular injection. The relative expression levels of miR-384-5p in the brain were evaluated by qRT-PCR. Compared to the NC group, miR-384-5p was overexpressed in the PFC (1.65 ± 0.42 SH group .P = 0.770) and rearing frequency , distance traveled (P = 0.140), and time spent in the target platform quadrant , with a thick and well-organized pyramidal layer harboring high density of neurons. No obvious changes in brain structure were observed, and the number of cells was similar between groups  regions of the Hip, as well as in the PFC and Str 2 weeks after treatment . HematoxP = 0.463) and rearing frequency (P = 0.263) did not differ between miR-SH and NC-SH groups . Furthermore, time spent searching for the platform was longer on days 3 and 4 . In the spatial probe test, there were no differences in the number of annulus crossing (P = 0.888), distance (P = 0.890), and time spent in the target platform quadrant (P = 0.665) between the two groups (n = 4 each). Values for neuronal degeneration and reduction in the number of neurons were similar between groups in the various brain regions studied  in the PFC (P = 0.041) but not in the other brain areas evaluated . DRD1 protein expression was 1.51-fold higher in the PFC of the miR-SH group  or mutated (DRD1-Mut) forms of these binding sites in HEK-293T cells, using the luciferase reporter assay. We found that transfection with miR-384-5p and DRD1-WT plasmids reduced luciferase activity  compared with the control  and p-CREB level was 1.80-fold higher , whereas there was no change in total CREB in the PFC (P = 0.022) and p-CREB/CREB ratio  were lower in the miR-SH group (n = 4 per group).We examined whether miR-384-5p overexpression in the PFC caused changes in DAT and CREB protein expression levels and found that in comparison to the NC group, in the miR-OV group, DAT level was 0.62-fold lower -A activity, as well as CREB phosphorylation in rats  for optimal performance . PreviouIn conclusion, we showed that miR-384-5p possibly negatively regulates DRD1 expression. This enhances dopaminergic neurotransmission, leads to an increase in CREB phosphorylation, and improves spatial learning and memory in SHR rats. However, ADHD is a complex polygenic hereditary disorder. The interaction between multiple susceptible genes and environmental factors cannot be dismissed. Additional studies are needed to fully elucidate the role of dopaminergic neurotransmission in ADHD.The datasets generated for this study are available on request to the corresponding author.The study protocol was approved by the Animal Ethics Committee of Nanjing Medical University (No. IACUC-1711004).QX, JO, and QZ performed and managed the experiments, as well as performed the statistical analyses. LY and RT analyzed the data. QX wrote the first draft of the manuscript. JW and QH revised the manuscript. XG, MT, LY, and XC contributed to study design and interpretation of results. All authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.